D465 Data Applications
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Free D465 Data Applications Questions
To create bullet points in their output document, a data analyst adds _____ to their R Markdown document.
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Asterisks
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Hashtags
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Brackets
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Spaces
Explanation
Explanation:
In R Markdown, asterisks (*) are used to create bullet points in lists. Each asterisk followed by a space represents a new item in the list. This syntax allows analysts to easily format text as unordered lists, which are helpful for summarizing information or presenting steps and observations clearly in the output document.
Correct Answer:
Asterisks
How can businesses use Natural Language Processing (NLP) to improve customer experience?
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By analyzing customer reviews and feedback to understand sentiment
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By converting images into numerical data
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By calculating transaction totals manually
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By developing algorithms for data compression
Explanation
Explanation:
Natural Language Processing (NLP) allows businesses to automatically analyze customer feedback, reviews, and chat interactions to understand sentiment and satisfaction levels. By detecting positive, negative, or neutral emotions in textual data, companies can identify pain points and improve customer service strategies. This enables more personalized communication and data-driven decision-making to enhance overall customer experience.
Correct Answer:
By analyzing customer reviews and feedback to understand sentiment
What is one primary purpose of using data analytics in sports marketing?
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Enhancing fan engagement
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Designing sports uniforms
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Negotiating athlete contracts
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Analyzing player performance
Explanation
Explanation:
In sports marketing, data analytics is primarily used to enhance fan engagement. By analyzing ticket sales, social media interactions, merchandise purchases, and fan behavior, teams and brands can tailor marketing campaigns, promotions, and personalized content to improve fan experience and loyalty. While player performance and contract negotiations are analytics applications, they fall under sports management and operations rather than marketing.
Correct Answer:
Enhancing fan engagement
What is the primary function of machine learning in data science?
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To visualize data trends
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To enable computers to learn from data and make predictions
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To clean and transform raw data
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To perform statistical analysis
Explanation
Explanation:
The primary function of machine learning in data science is to enable computers to automatically learn from data and make predictions or decisions without being explicitly programmed. Machine learning algorithms analyze large datasets to identify patterns and relationships, which they then use to predict outcomes or classify new data. This capability is essential in applications such as recommendation systems, fraud detection, speech recognition, and medical diagnosis. By allowing systems to continuously improve their performance as more data becomes available, machine learning forms the foundation for intelligent, data-driven solutions in modern analytics.
Correct Answer:
To enable computers to learn from data and make predictions
In the field of transportation, what is one of the most valuable uses of big data analytics?
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Predicting traffic congestion and optimizing route planning
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Storing transport schedules in spreadsheets
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Reducing data collection from GPS systems
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Analyzing only past traffic without considering real-time updates
Explanation
Explanation:
Big data analytics in transportation allows for the prediction of traffic congestion and route optimization by processing large volumes of real-time data from GPS sensors, traffic cameras, and vehicle tracking systems. By analyzing these data streams, transportation agencies can anticipate bottlenecks, manage traffic flow, and recommend optimal routes to drivers. This improves travel efficiency and reduces fuel consumption and emissions.
Correct Answer:
Predicting traffic congestion and optimizing route planning
Explain how machine learning differs from traditional programming methods in data analysis
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Machine learning uses algorithms to process data without explicit programming for each task.
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Machine learning requires more manual coding than traditional programming.
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Machine learning is only applicable in e-commerce.
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Machine learning does not involve data at all.
Explanation
Explanation:
Machine learning differs from traditional programming because it allows computers to learn patterns from data and make predictions without being explicitly programmed for each specific task. In traditional programming, rules and logic are manually defined by a programmer, while in machine learning, algorithms automatically identify patterns within datasets to generate predictive or classification models. This makes machine learning more adaptive and capable of handling complex, data-driven problems such as image recognition, fraud detection, or recommendation systems. It represents a shift from static rule-based systems to dynamic models that continuously improve with new data.
Correct Answer:
Machine learning uses algorithms to process data without explicit programming for each task
A company is struggling to manage its growing data from various sources. As a data engineer, which approach would you recommend to improve their data collection and storage processes?
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Implement a centralized data warehouse to consolidate data from different sources.
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Focus solely on data visualization tools to analyze existing data.
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Limit data collection to only the most recent data points.
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Use manual data entry to ensure accuracy.
Explanation
Explanation:
Implementing a centralized data warehouse is the most effective approach for managing growing data from multiple sources. A data warehouse integrates and stores data from various systems—such as sales, marketing, and customer platforms—into a single, organized repository. This centralized structure ensures data consistency, improves accessibility, and enables efficient querying and analysis across departments. By automating data collection and storage processes, it also reduces redundancy and enhances data quality. Ultimately, a data warehouse supports better decision-making by providing a unified, reliable view of organizational data.
Correct Answer:
Implement a centralized data warehouse to consolidate data from different sources
Which of the following concepts serves as the foundational basis for data science?
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Machine Learning
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Statistics and Probability
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Data Visualization
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Natural Language Processing
Explanation
Explanation:
Statistics and probability form the foundational basis of data science because they provide the mathematical framework for understanding, analyzing, and interpreting data. Concepts such as distributions, hypothesis testing, correlation, and regression are essential for making inferences and validating models. Probability theory helps data scientists assess uncertainty and make predictions, while statistical methods ensure that data-driven decisions are accurate and reliable.
Correct Answer:
Statistics and Probability
What is the primary purpose of data wrangling in data science?
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To visualize data for better understanding
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To clean and transform raw data into a usable format
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To apply machine learning algorithms
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To store data in databases
Explanation
Explanation:
Data wrangling, also known as data munging, is the process of cleaning, organizing, and transforming raw data into a structured and usable format. Raw data often contains inconsistencies, missing values, and errors that can lead to incorrect analysis if not handled properly. The goal of data wrangling is to prepare the data so that it can be accurately analyzed, modeled, or visualized in later stages of a data science project. This step is crucial because the quality of data directly influences the reliability of insights and predictions drawn from it.
Correct Answer:
To clean and transform raw data into a usable format
You are compiling an analysis of the average monthly costs for your company. What summary statistic function should you use to calculate the average?
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mean()
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min()
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max()
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cor()
Explanation
Explanation:
The mean() function in R is used to calculate the average of a set of numeric values. For example, when analyzing monthly costs, mean() provides a single value representing the central tendency of the data. Other functions, like min() and max(), return the smallest or largest values, and cor() calculates correlations, so they do not compute the average. Using mean() is the standard approach for summarizing data with a single representative value.
Correct Answer:
mean()
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